clf = DecisionTreeClassifier(random_state=0) rfc = RandomForestClassifier(random_state=0) #和决策树的random_state有所不同 #3.训练fit clf = clf.fit(x_train , y_train) rfc = rfc.fit(x_train , y_train) #4.测试score score_clf = clf.score(x_test , y_test) score_rfc = rfc.score...
fromsklearn.model_selectionimporttrain_test_split xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3) # 构建模型训练数据集 clf=DecisionTreeClassifier(random_state=0) rfc=RandomForestClassifier(random_state=0) clf=clf.fit(xtrain,ytrain) rfc=rfc.fit(xtrain,ytrain) # 进行模型评估...
1)}#对于min_samples_split和min_samples_leaf,一般是从他们的最小值开始向上增加10或20#面对高维度高样本量数据,如果不放心,也可以直接+50,对于大型数据,可能需要200~300的范围#如果调整的时候发现准确率无论如何都上不来,那可以放心大胆调一个很大的数据,大力限制模型的复杂度rfc = RandomForestClassifier(n_es...
model = RandomForestClassifier() model.fit(X_train, y_train) # 使用 pickle 保存模型 with open('./random_forest_model.pkl', 'wb') as file: pickle.dump(model, file) 模型保存为pkl文件: 2.3 模型推理与评价 加载训练好的模型(文件),输入测试集进行预测: # 加载保存的模型withopen('./random_fo...
#目的是带大家复习一下交叉验证#交叉验证:是数据集划分为n分,依次取每一份做测试集,每n-1份做训练集,多次训练模型以观测模型稳定性的方法fromsklearn.model_selectionimportcross_val_scoreimportmatplotlib.pyplot as plt rfc= RandomForestClassifier(n_estimators=25) ...
class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, bootstrap=True, oob_score=False, n_jobs=1, rand...
百度试题 结果1 题目sklearn.ensemble.RandomForestClassifier()是一种数据降维方法。()A.对B.错 相关知识点: 试题来源: 解析 B 反馈 收藏
本文简要介绍python语言中sklearn.ensemble.RandomForestClassifier的用法。 用法: classsklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, mi...
Pipeline from sklearn.decomposition import PCA from sklearn.ensemble import RandomForestClassifier ...
from sklearn.ensembleimportRandomForestClassifier# 导入随机森林分类器 from sklearn.datasetsimportload_wine from sklearn.model_selectionimporttrain_test_split from sklearn.model_selectionimportcross_val_score# 交叉验证的包importmatplotlib.pyplotasplt# 画图用的 ...